基于支持向量机的嫌疑人特征预测

Suspect Characteristics Prediction Based on Support Vector Machine

针对大数据环境下,公安机关计算机核心技术应用不足、备选嫌疑人众多而预测方法相对落后的问题,提出运用支持向量机(SVM)预测犯罪嫌疑人的模型。根据历史犯罪记录进行特征选择,训练基于SVM的嫌疑人特征预测模型,通过此模型对案件嫌疑人的各个特征进行预测,将预测出的特征与备选嫌疑人库中人员特征进行相似度计算,进而预测出最有可能的嫌疑人。实验结果表明,与应用分类和回归算法的模型相比,该模型对预测结果具有较好的解释性,能够缩小排查范围。

In the big data environment,the computer core technology of public security organs is insufficient,many alternative suspects and the forecast method is relatively backward problem, aiming at these problems,t his paper proposes the model of using the Support Vector Machine（ SVM） to predict the suspect. According to the historical crime record,the model carries on the feature selection firstly,based on SVM training the suspect characteristic prediction model.Through this model,it can predict the various characteristics of the suspect,and calculate the similarity between the characteristics of prediction and the staff characteristics of the suspect database,then can predict the most possible suspects. Experimental results show that compare to the previous models which using classification and regression algorithm,this model has a good explanation for the prediction results; Besides,it can narrow the scope of the investigation.